Byun Gihoon, Kim J S, Cho Chomgun, Song H C, Byun Sung-Hoon
Department of Convergence Study on the Ocean Science and Technology, Korea Maritime and Ocean University, Busan, 606-791, Korea
Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA
J Acoust Soc Am. 2017 Sep;142(3):EL286. doi: 10.1121/1.5003327.
The feasibility of tracking a ship radiating random and anisotropic noise is investigated using ray-based blind deconvolution (RBD) and array invariant (AI) with a vertical array in shallow water. This work is motivated by a recent report [Byun, Verlinden, and Sabra, J. Acoust. Soc. Am. 141, 797-807 (2017)] that RBD can be applied to ships of opportunity to estimate the Green's function. Subsequently, the AI developed for robust source-range estimation in shallow water can be applied to the estimated Green's function via RBD, exploiting multipath arrivals separated in beam angle and travel time. In this letter, a combination of the RBD and AI is demonstrated to localize and track a ship of opportunity (200-900 Hz) to within a 5% standard deviation of the relative range error along a track at ranges of 1.8-3.4 km, using a 16-element, 56-m long vertical array in approximately 100-m deep shallow water.
利用基于射线的盲反卷积（RBD）和阵列不变量（AI），结合浅水中的垂直阵列，研究了跟踪辐射随机各向异性噪声的船舶的可行性。这项工作的灵感来自最近的一份报告[Byun、Verlinden和Sabra，《美国声学学会杂志》141，797 - 807（2017）]，该报告指出RBD可应用于机会船舶以估计格林函数。随后，为浅水中稳健的源距离估计而开发的AI可通过RBD应用于估计的格林函数，利用在波束角和传播时间上分离的多径到达。在这封信中，通过在约100米深的浅水中使用一个16元、56米长的垂直阵列，证明了RBD和AI的组合能够在1.8 - 3.4千米的距离范围内，沿着一条轨迹将一艘机会船舶（200 - 900赫兹）定位并跟踪到相对距离误差的5%标准偏差以内。
